To provide a strong foundation on fundamental concepts in Computational Intelligence.
To enable Problem-solving through various searching techniques.
To apply these techniques in applications which involve perception, reasoning and learning.
To apply Computational Intelligence techniques for information retrieval
To apply Computational Intelligence techniques primarily for machine learning.
UNIT I INTRODUCTION 9
Introduction to Artificial Intelligence-Search-Heuristic Search-A* algorithm-Game Playing- Alpha-Beta Pruning-Expert systems-Inference-Rules-Forward Chaining and Backward Chaining- Genetic Algorithms.
UNIT II KNOWLEDGE REPRESENTATION AND REASONING 9
Proposition Logic – First Order Predicate Logic – Unification – Forward Chaining -Backward Chaining – Resolution – Knowledge Representation – Ontological Engineering – Categories and Objects – Events – Mental Events and Mental Objects – Reasoning Systems for Categories – Reasoning with Default Information – Prolog Programming.
UNIT III UNCERTAINTY 9
Non monotonic reasoning-Fuzzy Logic-Fuzzy rules-fuzzy inference-Temporal Logic-Temporal Reasoning-Neural Networks-Neuro-fuzzy Inference.
UNIT IV LEARNING 9
Probability basics – Bayes Rule and its Applications – Bayesian Networks – Exact and Approximate Inference in Bayesian Networks – Hidden Markov Models – Forms of Learning – Supervised Learning – Learning Decision Trees – Regression and Classification with Linear Models – Artificial Neural Networks – Nonparametric Models – Support Vector Machines – Statistical Learning – Learning with Complete Data – Learning with Hidden Variables- The EM Algorithm – Reinforcement Learning
UNIT V INTELLIGENCE AND APPLICATIONS 9
Natural language processing-Morphological Analysis-Syntax analysis-Semantic Analysis-AIl applications – Language Models – Information Retrieval – Information Extraction – Machine Translation – Machine Learning – Symbol-Based – Machine Learning: Connectionist – Machine Learning.
TOTAL: 45 PERIODS
OUTCOMES: Upon completion of the course, the students will be able to
Provide a basic exposition to the goals and methods of Computational Intelligence.
Study of the design of intelligent computational techniques.
Apply the Intelligent techniques for problem solving
Improve problem solving skills using the acquired knowledge in the areas of, reasoning, natural language understanding, computer vision, automatic programming and machine learning.
- Stuart Russell, Peter Norvig, ―Artificial Intelligence: A Modern Approach‖, Third Edition, Pearson Education / Prentice Hall of India, 2010.
- Elaine Rich and Kevin Knight, ―Artificial Intelligence‖, Third Edition, Tata McGraw-Hill, 2010.
- Patrick H. Winston. “Artificial Intelligence”, Third edition, Pearson Edition, 2006.
- Dan W.Patterson, ―Introduction to Artificial Intelligence and Expert Systems‖, PHI, 2006.
- Nils J. Nilsson, ―Artificial Intelligence: A new Synthesis‖, Harcourt Asia Pvt. Ltd., 2000.